Monte Carlo Techniques for Bayesian Statistical Inference – A comparative review
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چکیده
In this article, we summariseMonte Carlo simulationmethods commonly used in Bayesian statistical computing. We give descriptions for each algorithm and provide R codes for their implementation via a simple 2-dimensional example. We compare the relative merits of these methods qualitatively by considering their general user-friendliness, and numerically in terms of mean squared error and computational time. We conclude with some general guidelines and recommendations. Some keywords: Monte Carlo; Markov Chain Monte Carlo; Simulation; Rejection Sampling; Importance Sampling; Gibbs Sampler; Metropolis-Hastings; Adaptive Rejection; Slice Sampler; Sequential Monte Carlo.
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تاریخ انتشار 2007